class_docstr: Post-processing methods for ABCNet.
  
  Args:
      num_classes (int): Number of classes.
      use_sigmoid_cls (bool): Whether to use sigmoid for classification.
      strides (tuple): Strides of each feature map.
      norm_by_strides (bool): Whether to normalize the regression targets by
          the strides.
      bbox_coder (dict): Config dict for bbox coder.
      text_repr_type (str): Text representation type, 'poly' or 'quad'.
      with_bezier (bool): Whether to use bezier curve for text detection.
      train_cfg (dict): Config dict for training.
      test_cfg (dict): Config dict for testing.
docstr: Postprocess pred_results according to metainfos in data_samples.
  
  Args:
      pred_results (Union[Tensor, List[Tensor]]): The prediction results
          stored in a tensor or a list of tensor. Usually each item to
          be post-processed is expected to be a batched tensor.
      data_samples (list[TextDetDataSample]): Batch of data_samples,
          each corresponding to a prediction result.
      training (bool): Whether the model is in training mode. Defaults to
          False.
  
  Returns:
      list[TextDetDataSample]: Batch of post-processed datasamples.
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code: def __call__(
      self,
      spotting_data_samples,
      recog_data_samples,
      training: bool = False,
  ):
      """Postprocess pred_results according to metainfos in data_samples.
  
      Args:
          pred_results (Union[Tensor, List[Tensor]]): The prediction results
              stored in a tensor or a list of tensor. Usually each item to
              be post-processed is expected to be a batched tensor.
          data_samples (list[TextDetDataSample]): Batch of data_samples,
              each corresponding to a prediction result.
          training (bool): Whether the model is in training mode. Defaults to
              False.
  
      Returns:
          list[TextDetDataSample]: Batch of post-processed datasamples.
      """
      spotting_data_samples = list(
          map(self._process_single, spotting_data_samples)
      )
      return self.merge_predict(
          spotting_data_samples, recog_data_samples
      )
  
file_path: mmocr/projects/ABCNet/abcnet/model/abcnet_postprocessor.py
imports: from mmocr.models.textdet.postprocessors.base import BaseTextDetPostProcessor
  from mmocr.registry import MODELS
  from ..utils import bezier2poly
label_desc: Use image node_id 3 for calling the ABCNetPostprocessor obj's underlying member method code with example usage: obj.__call__(spotting_data_samples, recog_data_samples, training) and returns: self